Measuring the Impact of a Moving Target: Towards a Dynamic Framework for Evaluating Collaborative Adaptive Interactive Technologies
Bibliographic record
Abstract
BACKGROUND: Website evaluation is a key issue for researchers, organizations, and others responsible for designing, maintaining, endorsing, approving, and/or assessing the use and impact of interventions designed to influence health and health services. Traditionally, these evaluations have included elements such as content credibility, interface usability, and overall design aesthetics. With the emergence of collaborative, adaptive, and interactive ("Web 2.0") technologies such as wikis and other forms of social networking applications, these metrics may no longer be sufficient to adequately assess the quality, use or impact of a health website. Collaborative, adaptive, interactive applications support different ways for people to interact with health information on the Web, including the potential for increased user participation in the design, creation, and maintenance of such sites. OBJECTIVE: We propose a framework that addresses how to evaluate collaborative, adaptive, and interactive applications. METHODS: In this paper, we conducted a comprehensive review of a variety of databases using terminology related to this area. RESULTS: We present a review of evaluation frameworks and also propose a framework that incorporates collaborative, adaptive, and interactive technologies, grounded in evaluation theory. CONCLUSION: This framework can be applied by researchers who wish to compare Web-based interventions, non-profit organizations, and clinical groups who aim to provide health information and support about a particular health concern via the Web, and decisions about funding grants by agencies interested in the role of social networks and collaborative, adaptive, and interactive technologies technologies to improve health and the health system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.405 | 0.431 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.035 | 0.024 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.034 | 0.022 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".